Color interpolation apparatus and color interpolation method utilizing edge indicators adjusted by stochastic adjustment factors to reconstruct missing colors for image pixels

ABSTRACT

A color interpolation method for processing a plurality of image pixels corresponding to a color filter array to reconstruct missing color components for each image pixel having a single color component. The color interpolation method includes: (a) detecting edges in a plurality of interpolating directions for a target image pixel and then generating a plurality of edge indicators respectively corresponding to the interpolating directions, wherein the edge indicators have been normalized by stochastic adjustment factors; and (b) reconstructing at least a missing color component of the target image pixel according to a plurality of neighboring image pixels respectively in the interpolating directions and the edge indicators of the interpolating directions, wherein each of the neighboring image pixels has a color component identical to the missing color component.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to image processing, and morespecifically, to a color interpolation apparatus and color interpolationmethod using edge indicators adjusted by stochastic adjustment factorsto reconstruct missing colors for image pixels.

2. Description of the Prior Art

In an imaging system, three components of color information must becaptured simultaneously to precisely presenting an image. To create ananalogous digital imaging system that simultaneously captures all threecomponents of the color information would require three individualimaging detectors. This would be prohibitive due to the high cost andwould cause the packaging to be very complex. To keep the size and costof a digital video imaging system to a minimum, an image sensor array ofthe system (using silicon chips) must keep its size small as well.Therefore, the number of color samples must be kept low. An alternativeapproach is to have each detector of the imaging system gathering datafor a single color to create a sparse color image. Therefore, theimaging systems typically use a mosaic filter generally called a colorfilter array (CFA), and acquire a scene image by sampling one of thethree different color components to obtain an array that stores only onecolor component per pixel. The imaging system gets a raw sensory datahaving less color samples per pixel because it ignores the other twocolor components for each pixel. Since each filter of the color filterarray covers a single pixel and only allows a color in a specificspectral band to pass, before the scene image is further processed ordisplayed, the missing colors of each image pixel must be reconstructedso that each image pixel contains all three color components.

Because of the tri-stimulus nature of human color perception, toreconstruct a full color image (typically red, green, and blue or cyan,magenta, and yellow) from the raw sensory data, a color interpolationscheme is required to estimate these two missing color components foreach of the image pixels. Based on an assumption that an object in thephysical world usually gives rise to image intensities that varysmoothly over the image of the object, in order to economize processingresources, color interpolation is typically based upon color informationat image pixels in a small neighborhood around each image pixel to bereconstructed. Given a known color filter array (CFA), such as Bayerpattern described in U.S. Pat. No. 3,971,065, the remaining two colorsfor each image pixel location can be reconstructed using colorinformation provided by neighboring image pixels. The conventional colorinterpolation method uses replication of the values of the nearestneighboring image pixels, or alternatively uses linear or logarithmicaveraging techniques for obtaining an average value of the neighboringimage pixels for reconstructing the missing color. The colorinterpolation process to convert raw sensory image data into a fullcolor image by estimating the missing color components of each imagepixel from its neighboring image pixels is known as Demosaicing. Due tothe aliasing effects caused by averaging (low-pass filtering) pixelvalues across the edges, most demosaicing approaches often introduceimage effect problems like: zipper effects, false colors, or blur theedges of the image where there are dense edges. There are someconventional color interpolation methods to reduce or solve the imageeffect problems mentioned, such as: bilinear method, color differencebased method, gradient-based method, and C2D2 (Color Correlations andDirectional Derivatives) method. Most of the conventional methods can beclassified into two categories: non-adaptive color interpolation oredge-directed adaptive color interpolation.

The non-adaptive color interpolation method involves the colorinterpolation as mentioned above, uniformly applied across the entireimage, while the adaptive color interpolation, on the other hand,adjusts the weights of the color samples in the color interpolationprocess by utilizing the edge information. Moreover, a real-timeapplication commonly requires an interpolation algorithm with minimumcomputational complexity to provide high quality images that are sharpand do not contain false color. The false color usually occurs aroundthe edges of an image due to high spatial frequency. Localized spatialfeatures, such as image edges and areas where hues of adjacent pixelschange abruptly, cause estimation of a color to often be inaccurate.Further, false color usually occurs when one of the color components ismissing making imaging systems that utilizes a CFA to acquire imagesparticularly susceptible to this problem. Moreover, to provide sharpimages with no false color, a well-known sharpening filter is usuallyapplied to improve the image quality before displaying the image.However, when some color components are missing for each pixel, thecolor artifacts are not corrected with a sharpening filter, but aretypically further visually enhanced after a sharpening filter isapplied.

Therefore, the effectiveness of an adaptive color interpolation methodhighly depends on the edge detecting capability and the computationalcomplexity of the interpolation algorithm when realizing the imagingsystem. In other words, the edge detecting capability plays an importantrole in color interpolation. Taking the C2D2 method for example, thismethod suffers from blurred edges and false colors. Therefore, there isa need for a color interpolation algorithm for digital imaging systemsthat uses fewer samples per image pixel yet can accurately reconstructthe missing color components to provide sharp images without zippereffects or false colors.

SUMMARY OF THE INVENTION

It is therefore one of the objectives of the claimed invention toprovide a color interpolation apparatus and color interpolation methodusing edge indicators adjusted by stochastic adjustment factors toaccurately reconstruct missing colors for image pixels, to solve theabove problems.

According to one embodiment of the claimed invention, a colorinterpolation apparatus for processing a plurality of image pixelscorresponding to a color filter array to reconstruct missing colorcomponents for each image pixel having a single color component isdisclosed. The color interpolation apparatus comprises: an edgedetection circuit for detecting edges in a plurality of interpolatingdirections for a target image pixel and then generating a plurality ofedge indicators respectively corresponding to the interpolatingdirections by summing absolute values of a first color difference and asecond color difference and then normalizing the sum of the absolutevalues with a stochastic adjustment factor for normalizing standarddeviations of the interpolating directions, where the first colordifference is the difference between color components of a first imagepixel in the interpolating direction and a second image pixel in anotherinterpolating direction opposite to the interpolating direction and thesecond color difference is difference between color components of athird image pixel in the interpolation direction and the target imagepixel, (the color components of the first and second image pixels beingidentical to the missing color component of the target image pixel, thecolor component of the third image pixel being identical to the colorcomponent of the target image pixel); and an interpolation circuit,coupled to the edge detection circuit, for reconstructing at least amissing color component of the target image pixel according to aplurality of neighboring image pixels respectively in the interpolatingdirections and the edge indicators of the interpolating directions, eachof the neighboring image pixels having a color component identical tothe missing color component.

According to another embodiment of the claimed invention, a colorinterpolation method for processing a plurality of image pixelscorresponding to a color filter array to reconstruct missing colorcomponents for each image pixel having a single color component isdisclosed. The color interpolation method comprises: (a) detecting edgesin a plurality of interpolating directions for a target image pixel andthen generating a plurality of edge indicators respectivelycorresponding to the interpolating directions, wherein an edge indicatorfor each interpolating direction is determined by summing absolutevalues of a first color difference and a second color difference andthen normalizing a sum of the absolute values with a stochasticadjustment factor for normalizing standard deviations of theinterpolating directions, where the first color difference is betweencolor components of a first image pixel in the interpolating directionand a second image pixel in another interpolating direction opposite tothe interpolating direction, the second color difference is betweencolor components of a third image pixel in the interpolation directionand the target image pixel, (the color components of the first andsecond image pixels are identical to the missing color component of thetarget image pixel, the color component of the third image pixel areidentical to the color component of the target image pixel); and (b)reconstructing at least a missing color component of the target imagepixel according to a plurality of neighboring image pixels respectivelyin the interpolating directions and the edge indicators of theinterpolating directions, wherein each of the neighboring image pixelshas a color component identical to the missing color component.

The claimed invention provides better edge-sensing capabilities andfiner images. It reconstructs the missing color components of each imagepixel with high accuracy, because it is based on the detection of thespatial features present in the pixel neighborhood. Through thedirectionally edge-adaptive weighted color interpolation, it providessharp images without false colors and with smooth transition in hue frompixel to pixel. The directionally edge-adaptive weighted colorinterpolation of the claimed invention can be applied to image capturingdevices such as a digital camera or a digital video (DV) camcorder.

These and other objectives of the present invention will no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiment that isillustrated in the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an imaging system according to anembodiment of the present invention.

FIG. 2 is a diagram illustrating a color pattern of a color filter arrayshown in FIG. 1.

FIG. 3 is a block diagram of a color interpolation processor shown inFIG. 1.

FIG. 4 is a flowchart illustrating the operation of reconstructingmissing color components of a target image pixel according to anembodiment of the present invention.

DETAILED DESCRIPTION

Please refer to FIG. 1. FIG. 1 is a block diagram illustrating animaging system 10 for image capturing in a digital camera or a digitalvideo (DV) camcorder according to an embodiment of the presentinvention. The imaging system 10 comprises a lens 11 for opticallyreceiving an image; a color filter array (CFA) 12 having a specificcolor pattern to filter the image passing through the lens 11; an imagesensor array 13 to sense output of the CFA 12; and a color interpolationprocessor 14 for performing the directional edge-adaptive weighted colorinterpolation of the present invention to reconstruct missing colorcomponents for each image pixel to get a full color image.

Please refer to FIG. 2. FIG. 2 is a diagram illustrating a color pattern20 of the CFA 12 shown in FIG. 1. In this embodiment, the color pattern20 is a Bayer pattern. However, please note that the present inventionis not limited to using the Bayer pattern. As shown in FIG. 2, a missingcolor of a target image pixel B₄₄ is to be reconstructed. The colorinterpolation of the present invention makes use of twelve interpolatingdirections D1-D12 for reconstructing a missing green color. Based on theknown Bayer pattern, the target pixel B₄₄ has a blue color component,the neighboring image pixels G₄₃, G₃₄, G₄₅, G₅₄, G₃₂, G₂₃, G₂₅, G₃₆,G₅₆, G₆₅, G₆₃, G₅₂ each having a green color component. In thisembodiment, the color interpolation processor 14 shown in FIG. 1 willperform the color interpolation in all twelve interpolating directionsD1-D12 because in each of the interpolating directions color values ofneighboring image pixels are green, thus all twelve neighboring imagepixels G₄₃, G₃₄, G₄₅, G₅₄, G₃₂, G₂₃, G₂₅, G₃₆, G₅₆, G₆₅, G₆₃, G₅₂ canprovide needed color values for interpolating the wanted color componentfor the target image pixel B₄₄. On the other hand, if the missing colorcomponent of the target image pixel B₄₄ to be reconstructed, is notgreen, but lets say for example is red, then only the four nearest imagepixels R₃₃, R₃₅, R₅₅, R₅₃ having the same red color component would beselected. The operation of the color interpolation processor 14 isdetailed as follows.

Please refer to FIG. 3 in conjunction with FIG. 2. FIG. 3 is a blockdiagram of the color interpolation processor 14 shown in FIG. 1. Thecolor interpolation processor 14 comprises an edge detection circuit 41and a weighted interpolation circuit 42. In this embodiment, the imagingsystem 10 processes a plurality of image pixels corresponding to thecolor filter array CFA 12 in FIG. 1 to reconstruct missing colorcomponents for each image pixel. The edge detection circuit 41 is usedto detect edges in the interpolating directions D1-D12 for the targetimage pixel B₄₄ and then generates a plurality of edge indicators I1-I12respectively corresponding to the interpolating directions D1-D12. Theweighted interpolation circuit 42, coupled to the edge detection circuit41, is used to reconstruct the missing green color component of thetarget image pixel B₄₄ according to the neighboring image pixels G₄₃,G₃₄, G₄₅, G₅₄, G₃₂, G₂₃, G₂₅, G₃₆, G₅₆, G₆₅, G₆₃, G₅₂ respectively inthe interpolating directions D1-D12. In addition, the weightedinterpolation circuit 42 is further used to reconstruct the missing redcolor component of the target image pixel B₄₄ according to theneighboring image pixels R₃₃, R₃₅, R₅₅, R₅₃.

The edge detection circuit 41 determines an edge indicator I1, I2, . . ., I12 for an interpolating direction D1, D2, . . . , D12 by summingabsolute values of a first color difference and a second colordifference with an adjustment process to increase the resolution of theedge indicators. The first color difference is the difference betweencolor components of a first image pixel in the interpolating directionand a second image pixel in another interpolating direction opposite tothe interpolating direction, and the second color difference is thedifference between color components of a third image pixel in theinterpolation direction and the target image pixel, where the colorcomponents of the first and second image pixels are identical to themissing color component of the target image pixel, and the colorcomponent of the third image pixel is identical to the color componentof the target image pixel.

For increasing the resolution of the edge indicators, according to theembodiment of the present invention a stochastic edge-detecting schemeis disclosed. The stochastic edge-detecting scheme has more preciseedge-sensing ability than prior art edge-detecting schemes, such as thedirectional-derivative method (e.g. linear adjustment in C2D2), and itcan be generalized to other sets of edge orientations. In the stochasticedge-detecting scheme, an image source having a locally stationaryGaussian distribution is utilized. Therefore, the image samples within alocally stationary region will have the same stochastic mean. Denote thecurrent image sample as X(0), and a sample d pixels away from thecurrent image sample X(0), as X(d). In this embodiment, image samplesX(0) and X(d) are both within the stationary region to have the samestochastic mean. Then, the difference X(0)−X(d) will be a Gaussianrandom variable with zero mean, and the probability that the differencevalue is largely away from zero will be very small. Since the goal ofthe edge-adaptive weighted color interpolation is to assign small weightto an image sample during the interpolation if the likeliness of an edgebetween the image sample and the target image pixel is high, also, sincethe probability distribution will be different when the differences arecalculated from image samples with different distance d, the edgeindicator in this embodiment is calculated by normalizing the differencevalue of the image samples, thereby generating a fair measure of theedge strength.

To normalize the difference values, in this embodiment, the calculateddifference values are multiplied by an adjustment factor κ such that themodified values become Gaussians with the same standard deviation (thuswith the same distribution).

The derivation of the factor κ is as follows. Let Ω(d) denote theauto-correlation function:Ω(d)=(E[(X(0)−μ_(x))(X(d)−μ_(x))])/(σ_(x) ²)  Eq.(1)

In Eq. (1), μ_(x) is the stochastic mean in the stationary region andσ_(x) is the standard deviation. The standard deviation of thedifference X(0)−X(d) will be: $\begin{matrix}\begin{matrix}{\sqrt{E\lbrack ( {{X(0)} - {X(d)}} )^{2} \rbrack} = \sqrt{E\lbrack ( {( {{X(0)} - \mu_{X}} ) - ( {{X(d)} - \mu_{X}} )} )^{2} \rbrack}} \\{= \sqrt{E\begin{bmatrix}{( {{X(0)} - \mu_{X}} )^{2} - {2( {{X(0)} - \mu_{X}} )}} \\{( {{X(d)} - \mu_{X}} ) + ( {{X(d)} - \mu_{X}} )^{2}}\end{bmatrix}}} \\{= \sqrt{\sigma_{X}^{2} - {2\quad\sigma_{X}^{2}{\Omega(d)}} + \sigma_{X}^{2}}} \\{= {\sigma_{X}\sqrt{( {1 - {\Omega(d)}} )}}}\end{matrix} & \begin{matrix}{{Eq}.\quad( {2.a} )} \\{{Eq}.\quad( {2.b} )} \\{{Eq}.\quad( {2.c} )} \\{{Eq}.\quad( {2.d} )}\end{matrix}\end{matrix}$

Eq. (2.c) holds because of the stationary property. If a certaindistance d₁ is set as the basic distance, for other distance d_(n), thenormalization factor κ_(n) will be: $\begin{matrix}{K_{n} = {\frac{\sigma_{\overset{\_}{X}}\sqrt{( {1 - {\Omega( d_{1} )}} )}}{\sigma_{\overset{\_}{X}}\sqrt{( {1 - {\Omega( d_{n} )}} )}} = \sqrt{\frac{( {1 - {\Omega( d_{1} )}} )}{( {1 - {\Omega( d_{n} )}} )}}}} & {{Eq}.\quad(3)}\end{matrix}$

κ_(n) can be derived when substituting Ω(d₁) and Ω(d₂) calculated by Eq.(1) into Eq. (3).

Taking the edge indicator I1 for example, it is calculated as follows:I1=κ₁*[ABS (the color value of G₄₃−the color value of G₄₅)+ABS (thecolor value of B₄₂−the color value of B₄₄)]  Eq.(4)

In Eq. (4), G₄₃ is the above-mentioned first image pixel, G₄₅ is theabove-mentioned second image pixel, and B₄₂ is the above-mentioned thirdimage pixel. Therefore, the distance d₁ equals 2 pixels. As shown inFIG. 2, the distances d₁-d₁₂ can be divided into two categories: d_(n)=2for 1≦n≦4 and d_(n)=2√{square root over (5)} for 5≦n≦12. So the value ofκ_(n) will be of two kinds. Under the assumption that theautocorrelation function Ω(d) is of the form Ω(d)=exp(−d²/τ²), where τis a image dependent factor, κ_(n) will be very close to 0.5, for5≦n≦12, as long as the value Ω(d₁) is high. The setting of the factorκ_(n) can be summarized as follows: $\begin{matrix}{K_{n} = \{ \begin{matrix}{1,} & {1 \leq n \leq 4} \\{0.5,} & {5 \leq n \leq 12}\end{matrix} } & {{Eq}.\quad(5)}\end{matrix}$

The calculation of the remaining edge indicators I2-I12 can be easilyderived from the above description, Eq. (4) and Eq. (5).

In the present invention, the weighted interpolation circuit 42 shown inFIG. 3 comprises a weight calculation module 43 and an interpolationmodule 44. The weight calculation module 43 is used to calculate aplurality of weights W1-W12 for the neighboring image pixels G₄₃, G₃₄,G₄₅, G₅₄, G₃₂, G₂₃, G₂₅, G₃₆, G₅₆, G₆₅, G₆₃, G₅₂ according to the edgeindicators I1-I12. The weights W₁-W₁₂ are calculated as follows:$\begin{matrix}{W_{n} = {( \frac{1}{1 + I_{n}} )/{\sum\limits_{n = 1}^{12}\frac{1}{1 + I_{n}}}}} & {{Eq}.\quad(6)}\end{matrix}$

Please note that Eq. (6) is not the only way to calculate the weights.In another embodiment, other kinds of functions or a look up table canalso calculate the weights, thus the present invention is not limited toEq. (6).

The interpolation module 44, coupled to the weight calculation module43, is used to reconstruct the missing green color component accordingto the weights W₁-W₁₂ calculated by Eq. (6) and color components of theneighboring image pixels G₄₃, G₃₄, G₄₅, G₅₄, G₃₂, G₂₃, G₂₅, G₃₆, G₅₆,G₆₅, G₆₃, G₅₂ by summing a plurality of products of the weights W1-W12and a plurality of color values of the neighboring image pixels G₄₃,G₃₄, G₄₅, G₅₄, G₃₂, G₂₃, G₂₅, G₃₆, G₅₆, G₆₅, G₆₃, G₅₂ to reconstruct themissing green color component for the target image pixel B₄₄. That is,the reconstructed green color component is equal to: W₁ * (color valueof G₄₃)+W₂* (color value of G₃₄)+, . . . , +W₁₂ * (color value of G₅₂).

However, as described in “Effective color interpolation in CCD colorfilter arrays using signal correlation, ” IEEE Trans. on Circuit andSystem for Video Technology, vol. 13, no. 6, June 2003, because of thehigh correlation between R, G, and B color components of the imagecaptured by the imaging system 10, the color difference values (G−R, G,G−B) are relatively smoother than the (R, G, B) values. As a result,this causes the aliasing effects to be less pronounced (i.e., lessvisible) in the color difference space. Thus, the interpolation module44 can apply color difference values (G−R, G, G−B) instead of (R, G, B)to reconstruct the missing green color component for better imagequality. That is, in another embodiment using color difference, thereconstructed green component is equal to: (color value ofB₄₄)+W₁*(color difference value between green color component and bluecolor component of G₄₃)+W2*(color difference value between green colorcomponent and blue color component of G₃₄)+ . . . +W₁₂*(color differencevalue between green color component and blue color component of G₅₂).

As to reconstructing the missing red color component of the target imagepixel B₄₄, the color interpolation operation is similar to that forreconstructing the missing green color component. Referring to Eq. (4)and the related description, the edge indicators I1′-I4′ are obtained bythe edge detection circuit 41. Similarly, referring to Eq. (6) and therelated description, the weights W₁′-W₄′ are obtained by the weightcalculation module 43. In one embodiment, the reconstructed red colorcomponent is equal to: W₁′* (color value of R₃₃)+W₂′* (color value ofR₃₅)+W₃′* (color value of R₅₅)+W₄′* (color value of R₅₃). In anotherembodiment using color difference, the reconstructed red color componentis equal to: (reconstructed green color value of B₄₄)−W₁′* (colordifference value between green color component and red color componentof R₃₃)−W₂′* (color difference value between green color component andred color component of R₃₅)−W₄′* (color difference value between greencolor component and red color component of R₅₃).

In the above description, the color interpolation applied toreconstructing the missing color components for the target image pixelB₄₄ has been detailed. Based on the above description, those skilled inthis art can easily realize that the same process can be applied toother pixels for reconstructing corresponding missing color components.Further description is omitted for brevity.

Please refer to FIG. 4. FIG. 4 is a flowchart illustrating the operationof reconstructing missing color components of a target image pixelaccording to an embodiment of the present invention. The operation ofthe directional edge-adaptive weighted color interpolation toreconstruct missing colors includes the following steps:

Step 300: Use a twelve-direction color Interpolation to reconstruct allmissing green components of all target pixels.

Step 302: Use a four-direction color Interpolation to reconstruct theother missing components of the target pixels in Step 300.

Step 304: Use the twelve-direction color Interpolation to reconstructmissing color components of remaining pixels. Please note that the colorinterpolation is performed by utilizing the hardware circuitry as shownin FIG. 1. However, the color interpolation can be implemented bysoftware computation as well. For instance, a processor and a storagedevice are implemented to build the desired color interpolationapparatus, where the storage device stores a color interpolationprogram. Therefore, when the color interpolation program is loaded andexecuted by the processor, the processor is capable of performing theabove-mentioned weighted color interpolation. In other words, theprocessor running the color interpolation program is functionalityidentical to that of a hardware-based color interpolation apparatusmentioned above.

In contrast to the related art, the present invention provides betteredge-sensing capability and finer images. It reconstructs the missingcolor components of each image pixel with high accuracy, because it isbased on the detection of the spatial features present in the pixelneighborhood. Through the directionally edge-adaptive weighted colorinterpolation, it provides sharp images without false colors and withsmooth transition in hue from pixel to pixel. The directionallyedge-adaptive weighted color interpolation of the present invention canbe applied to image capturing devices such as a digital camera or adigital video (DV) camcorder.

Those skilled in the art will readily observe that numerousmodifications and alterations of the device and method may be made whileretaining the teachings of the invention. Accordingly, the abovedisclosure should be construed as limited only by the metes and boundsof the appended claims.

1. A color interpolation apparatus for processing a plurality of imagepixels corresponding to a color filter array to reconstruct missingcolor components for each image pixel having a single color component,the color interpolation apparatus comprising: an edge detection circuitfor detecting edges in a plurality of interpolating directions for atarget image pixel and then generating a plurality of edge indicatorsrespectively corresponding to the interpolating directions, the edgedetection circuit determining an edge indicator for an interpolatingdirection by summing absolute values of a first color difference and asecond color difference and then determining the edge indicator for theinterpolating direction by normalizing a sum of the absolute values witha stochastic adjustment factor for normalizing standard deviations ofthe interpolating directions, the first color difference being betweencolor components of a first image pixel in the interpolating directionand a second image pixel in another interpolating direction opposite tothe interpolating direction, the second color difference being betweencolor components of a third image pixel in the interpolation directionand the target image pixel, the color components of the first and secondimage pixels being identical to the missing color component of thetarget image pixel, the color component of the third image pixel beingidentical to the color component of the target image pixel; and aninterpolation circuit, coupled to the edge detection circuit, forreconstructing at least a missing color component of the target imagepixel according to a plurality of neighboring image pixels respectivelyin the interpolating directions and the edge indicators of theinterpolating directions, each of the neighboring image pixels having acolor component identical to the missing color component.
 2. The colorinterpolation apparatus of claim 1, wherein the interpolation circuitcomprises: a weight calculation module for calculating a plurality ofweights for the neighboring image pixels according to the edgeindicators; and an interpolation module, coupled to the weightcalculation module, for reconstructing the missing color componentaccording to the weights and color components of the neighboring imagepixels.
 3. The color interpolation apparatus of claim 2, wherein theinterpolation module reconstructs the missing color component by summinga plurality of products of the weights and color components of theneighboring image pixels.
 4. The color interpolation apparatus of claim1, wherein the color filter array is a Bayer pattern.
 5. The colorinterpolation apparatus of claim 1 being applied to an image capturingdevice.
 6. The color interpolation apparatus of claim 5, wherein theimage capturing device is a digital camera or a digital video (DV)camcorder.
 7. A color interpolation method for processing a plurality ofimage pixels corresponding to a color filter array to reconstructmissing color components for each image pixel having a single colorcomponent, the color interpolation method comprising: (a) detectingedges in a plurality of interpolating directions for a target imagepixel and then generating a plurality of edge indicators respectivelycorresponding to the interpolating directions, wherein an edge indicatorfor an interpolating direction is determined by summing absolute valuesof a first color difference and a second color difference and thennormalizing a sum of the absolute values with a stochastic adjustmentfactor for normalizing standard deviations of the interpolatingdirections, the first color difference is between color components of afirst image pixel in the interpolating direction and a second imagepixel in another interpolating direction opposite to the interpolatingdirection, the second color difference is between color components of athird image pixel in the interpolation direction and the target imagepixel, the color components of the first and second image pixels areidentical to the missing color component of the target image pixel, thecolor component of the third image pixel are identical to the colorcomponent of the target image pixel; and (b) reconstructing at least amissing color component of the target image pixel according to aplurality of neighboring image pixels respectively in the interpolatingdirections and the edge indicators of the interpolating directions,wherein each of the neighboring image pixels has a color componentidentical to the missing color component.
 8. The color interpolationmethod of claim 7, wherein step (a) further comprises: (a1) calculatinga plurality of weights for the neighboring image pixels according to theedge indicators; and (a2) reconstructing the missing color componentaccording to the weights and color components of the neighboring imagepixels.
 9. The color interpolation method of claim 8, wherein step (a2)further comprises summing a plurality of products of the weights andcolor components of the neighboring image pixels.
 10. The colorinterpolation method of claim 7, wherein the color filter array is aBayer pattern.
 11. The color interpolation method of claim 7 beingapplied to an image capturing device.
 12. The color interpolation methodof claim 11, wherein the image capturing device is a digital camera or adigital video (DV) camcorder.